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Title: A recommendation system for scientific water data
Abstract

We describe a recommendation system for HydroShare, a platform for scientific water data sharing. We discuss similarities, differences and challenges for implementing recommendation systems for scientific water data sharing. We discuss and analyze the behaviors that scientists exhibit in using HydroShare as documented by users’ activity logs. Unlike entertainment system users, users on HydroShare tend to be task-oriented, where the set of tasks of interest can change over time, and older interests are sometimes no longer relevant. By validating recommendation approaches against user behavior as expressed in activity logs, we conclude that a combination of content-based filtering and a latent Dirichlet allocation (LDA) topic modeling of user behavior—rather than and instead of LDA classification of dataset topics—provides a workable solution for HydroShare and compares this approach to existing recommendation methods.

 
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Award ID(s):
1664061 1664018
NSF-PAR ID:
10387619
Author(s) / Creator(s):
;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
International Journal of Data Science and Analytics
Volume:
12
Issue:
1
ISSN:
2364-415X
Page Range / eLocation ID:
p. 61-75
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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